{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "S+P Week 4 Lesson 1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1J15Vh_1Jih",
        "colab_type": "code",
        "outputId": "711774e2-ee28-4e52-d308-bbed7822e72a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 698
        }
      },
      "source": [
        "!pip install tf-nightly-2.0-preview\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly-2.0-preview\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7c/33/a6fc8a3e3c53821d169baf98d755c755b0620a913a8f3872ef141e6322a1/tf_nightly_2.0_preview-2.0.0.dev20190628-cp36-cp36m-manylinux1_x86_64.whl (79.3MB)\n",
            "\u001b[K     |████████████████████████████████| 79.3MB 289kB/s \n",
            "\u001b[?25hRequirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (0.1.7)\n",
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            "Collecting tb-nightly<1.15.0a0,>=1.14.0a0 (from tf-nightly-2.0-preview)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3a/60/afa129c3621d62c885599076f8e89737d66e5dfffad1a08842b1c11b4540/tb_nightly-1.14.0a20190614-py3-none-any.whl (3.1MB)\n",
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            "Collecting opt-einsum>=2.3.2 (from tf-nightly-2.0-preview)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f6/d6/44792ec668bcda7d91913c75237314e688f70415ab2acd7172c845f0b24f/opt_einsum-2.3.2.tar.gz (59kB)\n",
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            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.11.2)\n",
            "Collecting tensorflow-estimator-2.0-preview (from tf-nightly-2.0-preview)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0b/ab/b4a3028395fcc98290c9516c8eb0adb1cb27d81a3df23db70ac1acee2249/tensorflow_estimator_2.0_preview-1.14.0.dev2019070200-py2.py3-none-any.whl (447kB)\n",
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            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly-2.0-preview) (41.0.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly-2.0-preview) (0.15.4)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly-2.0-preview) (3.1.1)\n",
            "Building wheels for collected packages: opt-einsum\n",
            "  Building wheel for opt-einsum (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Stored in directory: /root/.cache/pip/wheels/51/3e/a3/b351fae0cbf15373c2136a54a70f43fea5fe91d8168a5faaa4\n",
            "Successfully built opt-einsum\n",
            "Installing collected packages: tb-nightly, opt-einsum, tensorflow-estimator-2.0-preview, tf-nightly-2.0-preview\n",
            "Successfully installed opt-einsum-2.3.2 tb-nightly-1.14.0a20190614 tensorflow-estimator-2.0-preview-1.14.0.dev2019070200 tf-nightly-2.0-preview-2.0.0.dev20190628\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tensorboard",
                  "tensorflow",
                  "tensorflow_estimator"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOjujz601HcS",
        "colab_type": "code",
        "outputId": "a84d8da6-31f6-4914-8195-6373dc9c84ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-dev20190628\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zswl7jRtGzkk",
        "colab": {}
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(True)\n",
        "\n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "\n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.4,\n",
        "                    np.cos(season_time * 2 * np.pi),\n",
        "                    1 / np.exp(3 * season_time))\n",
        "\n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "\n",
        "def noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "\n",
        "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
        "baseline = 10\n",
        "series = trend(time, 0.1)  \n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "slope = 0.05\n",
        "noise_level = 5\n",
        "\n",
        "# Create the series\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "# Update with noise\n",
        "series += noise(time, noise_level, seed=42)\n",
        "\n",
        "split_time = 1000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 20\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sTTIOCbyShY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "    series = tf.expand_dims(series, axis=-1)\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
        "    ds = ds.shuffle(shuffle_buffer)\n",
        "    ds = ds.map(lambda w: (w[:-1], w[1:]))\n",
        "    return ds.batch(batch_size).prefetch(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_eaAX9g_jS5W",
        "colab": {}
      },
      "source": [
        "def model_forecast(model, series, window_size):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
        "    ds = ds.batch(32).prefetch(1)\n",
        "    forecast = model.predict(ds)\n",
        "    return forecast"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yqc2GTsps0qf",
        "colab_type": "code",
        "outputId": "6a57b2fa-e0df-4086-a525-fa08c3e052f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 820
        }
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "tf.random.set_seed(51)\n",
        "np.random.seed(51)\n",
        "\n",
        "window_size = 30\n",
        "train_set = windowed_dataset(x_train, window_size, batch_size=128, shuffle_buffer=shuffle_buffer_size)\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv1D(filters=32, kernel_size=5,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\",\n",
        "                      input_shape=[None, 1]),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True)),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True)),\n",
        "  tf.keras.layers.Dense(1),\n",
        "  tf.keras.layers.Lambda(lambda x: x * 200)\n",
        "])\n",
        "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
        "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0702 14:30:54.633586 139953869899648 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/data/util/random_seed.py:58: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "<PrefetchDataset shapes: ((None, None, 1), (None, None, 1)), types: (tf.float32, tf.float32)>\n",
            "(1000,)\n",
            "Epoch 1/100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0702 14:30:58.607080 139953869899648 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/optimizer_v2/optimizer_v2.py:460: BaseResourceVariable.constraint (from tensorflow.python.ops.resource_variable_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Apply a constraint manually following the optimizer update step.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "8/8 [==============================] - 5s 681ms/step - loss: 73.2800 - mae: 73.6896\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 1s 120ms/step - loss: 72.5471 - mae: 72.9788\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 1s 119ms/step - loss: 71.3986 - mae: 71.8386\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 1s 115ms/step - loss: 69.9652 - mae: 70.4125\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 1s 117ms/step - loss: 68.2942 - mae: 68.7483\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 1s 114ms/step - loss: 66.3956 - mae: 66.8575\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 1s 113ms/step - loss: 64.2604 - mae: 64.7319\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 1s 113ms/step - loss: 61.8746 - mae: 62.3584\n",
            "Epoch 9/100\n",
            "4/8 [==============>...............] - ETA: 0s - loss: 60.2353 - mae: 60.7341"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-61b062483ef8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m               \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m               metrics=[\"mae\"])\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlr_schedule\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    667\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 668\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    669\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m   def evaluate(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[1;32m    691\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m         steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m    694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m   def evaluate(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    293\u001b[0m       \u001b[0mbatch_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m       \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m       \u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m       \u001b[0mstep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/callbacks.py\u001b[0m in \u001b[0;36mon_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    756\u001b[0m     \u001b[0;31m# will be handled by on_epoch_end.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 758\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, current, values)\u001b[0m\n\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m       \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 456\u001b[0;31m       \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    457\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    458\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/iostream.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0mevt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthreading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m             \u001b[0mevt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    322\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjTvASUns0qh",
        "colab_type": "code",
        "outputId": "2363d859-2dce-437e-a8ec-092a340ce218",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "source": [
        "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
        "plt.axis([1e-8, 1e-4, 0, 30])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1e-08, 0.0001, 0, 30]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FoyNdknjAvqp6CrJSMGsL7Pystn75kqp6RuWmB/Q12pZgSOx4nJmcQFZKQlQsxaCWvkS1\n71wxkavOPIWfvrSJhUu3Rroc8YCSynry/ZOygI4JWsEM2yyraei4idtuaE73wzZrG5v59tNrO7qY\nwkGhL1Etzmf87HOTufz0fP7rbxt4ePn2SJck/VxJZX3HTVyAgqy2rplgRvAcrGlk4FEtfYAh2d1P\n0Fq59RB/WrWLx1bsDKLi4Cj0JerFx/n49RemcPGEPL777DoeXxW+/yHE21paHaVV9R3DNYGOVn8w\nN3MPdtLSb5+gdbKx+uv3VQHw/Nq9YRvTr9CXmJAQ5+N3103h/LG5fPuZ93nqXa3RI6F3sKaB5lZ3\nTEs/JTGOrJSEgIdt1jW2cLix5YSW/tCcVBqaWzu2UezMBn/obz9Yy7q9VT34Drqn0JeYkRQfx303\nnM3MUYP416fe4xktziYhdmRiVsoxx/MzkwNedK19m8RBaSe29OHk6+qv31fFtOE5xPmM59fuC7ju\nYHQb+ma22Mz2m9kHRx37vpntMbM1/o/Lw1KdyHGSE+K4/0tFnDtqIN988j3+8s89kS5J+pF9x03M\napeXFfis3PYN1QdlnNjSh66XWK5tbGZb2WHOGTWQmaMHha2LJ5CW/oPAZZ0c/6Vz7kz/xwuhLUuk\naymJcSz80lSmjxjI3U+s4dk1Cn4JjRL/qJn840K/IIiW/pHZuMe29Auz/S39Lm7mbiqpxjmYeEom\nV5xRwO7yOtburgyq/kB0G/rOuTcBbWkkUSUlMY5FNxUxdXgOdz2+Rl09EhL7qupJjPORk3psKz0v\nK5kDNQ00BTA7vKxjhc1jv0ZaUjw5aYldtvQ37Gtba2piQSYfm5hPQpzx/Nq9Pfk2Tqo3ffp3mtla\nf/fPgK5OMrP5ZlZsZsUHDhzoxduJHCs1MZ4Hbp7KjJEDufuJ93jwrW2RLkliXGllPXlZR2bStsvP\nTMa5thU4u1N23GJrRxs6IKXLWbnr91WSkRTPkAEpZKUmMGtMLn9buy/kXTw9Df17gFHAmcA+4Odd\nneicW+CcK3LOFeXm5vbw7UQ6l5oYz+KbpnLpxDy+/9f1/PqVj6Ji+VqJTfsq6zvW3DlaMNsmHqxp\nJD0pnuSEuBOeGzIg9aQt/QkFmR0zgT9xegF7K+tZvbMimG+hWz0KfedcqXOuxTnXCtwPTAtpVSJB\nSE6I4/fXncXVZw3hl698yA+e11670jMlx43RbxfMrNyymoYTunbajRqczs5DtVTUHjtss7XVsWFf\nFRNPyew4dslpeSTG+ULexdOj0DezgqMefhr4oKtzRfpCfJyPn372DG6eOZwH3trON598L6D+V5F2\nzrm2ln4nod9+LJAJWgcPnzgxq92c8YNpaXW8tnH/Mcd3HqqltrGFCQUZHccykxM4f1wuz63p49A3\nsz8Cy4FxZrbbzG4BfmJm75vZWuBC4K6QViXSAz6f8d0rJnL3JWN55p97uP2Rd6lvaol0WRIjymub\naGxu7bSln52aQGK8L6AJWgdrGjvW0T/e6YVZ5Gcm89K6kmOOt8/EnViQdczxK84o6BgCGirdrrLp\nnPtiJ4cXhbQKkRAxM746ZwwD0hL57rMfcMOilSy8cSpZKQmRLk2iXPsiZ0cvttbOzALeQauspoEp\nwzof2+LzGZeelscTxbuoa2whJbGt33/DvirifMaYvGNX8ZwzIS/kP7uakSv90g0zTuU3X5zCml0V\nfP6+5ewPcucj8Z6S47ZJPF5+VvebqbS0Og4dbiS3iz59gEsn5lPf1MqyzWUdx9bvrWLkoLQTbv6m\nJ8Xz5r9dGOi3EBCFvvRbV5xxCotvmsrOQ7V89t7l3a5wKN52ZDbuiaN3oO0vgO66dypqG2l1MLCL\nPn2A6SNzyEyOP6aL5/ibuEdTS18kCLPG5PLYvOlU1DZyzX3L2XKgJtIlSZQqqawnzmfkZnQe2PlZ\nbbNyTzYkuH2Mflejd6Bt8cA5E/J4dUMpzS2tVNQ2sreyngkFnYd+qCn0pd+bMmwAj992Dk0trVxz\n73LWh2n1Qolt+yrrGZyRRNxxE7Pa5Wcm09DcSkVtU5dfo30Jhq5G77S7dGIe5bVNFO8oP+omrkJf\nJGQmFGTy+G3nkBjv4wsLlrN6Z3mkS5Ioc/Bw1+Pr4ciwzb0n2dWqrH2xtZN8HYDZY3NJjPfxj3Wl\nHcsvqKUvEmKjctN58vZzGJCWyPULV7Jy68FIlyRRpLKuieyUrsO6fWRNe0h3pqw6sJZ+WlI8s0YP\n4qV1JazfW0VuRlKX3UqhptAXTxkyIJUnbzuHgqxkbnzgHd46agSFeFtlXdNJb5qOGJROWmIca3d3\nvSzCwcMNxPuMzOTub75+7LR89lTU8Y/1JX3WygeFvnjQ4Mxk/jT/HE7NSWPug6t440MtBChQWdtE\nVmrXYR3nMyYVZp10ueODNY3kpCWesGBbZ+ZMGIzPoLq+uc/680GhLx6Vm5HEH+fPYFRuOrc+VMyr\nG0ojXZJEkHOu25Y+wBlDsli/r6rLJT7KOtkbtysD05MoOjUH4JjlF8JNoS+elZOWyB9unc74ggxu\nf/RdBb+H1Ta20NzqyO4m9E8fkk1jcyubSjrv1y+raTzpzeDjXTYpv+3rFmZ1c2boKPTF07JTE3nk\nlulMKMjkXx5dra4ej6qoaxuG2V1Lf/KQtnB+f0/nXTwnW2ytMzeccypP3n4OI3PTuz85RBT64nlZ\nKQk8PHcaowenM//hYt3c9aDK2sBCf1hOKlkpCZ3ezK2sbWJvRX3HXriBSIjzMXV4TnDF9pJCX4S2\nFv+j86YzfGAa8x4q1nBOj6moaxtff7IbudC28NoZQzq/mbvkw/20tDouHBfdm0Up9EX8ctISeezW\n6RQOSOHmB1fxT03g8oyqALt3oK3/fVNJ9QnLdr+6YT+D0hOZPCQ7LDWGikJf5CiD0pP4w7zp5GYk\ncdMDq/iwtOuJONJ/VPpDPzu1+5uwZwzJptm/01W75pZWlmzaz4XjBgc0XDOSFPoixxmcmcyjt0wn\nKd7HDYtWanVOD6gIsE8f2oZtwrE3c4t3lFNV38ycCYPDU2AIKfRFOjE0J5VHbplOfVMrNyxayQH/\n9HrpnyrrmojzGWmJJ25mfryCrGQGpSfy3q4jof/axv0kxvk4b0x09+eDQl+kS+PyM1h801RKqxr4\n0uJ3OroApP+pqGsiOyUBs+67Ztpu5mbz/p4jI3he2VDK9JE5pCd1uxlhxCn0RU7i7FMHcN8NZ7N5\nfzW3PVJMQ7P23O2PApmNe7TTC7PYvL+Gww3NbCs7zNYDh5kzPvq7dkChL9Kt2WNz+elnJ7Ni6yH+\n7am1tLZ2vYmGxKaquiYygwj9yUOzaHWwbm9Vx0zuORPywlVeSEX/3yIiUeCqKYXsqajjpy9t4pTs\nFL512fhIlyQhVFHbFNTyCZP8yyas3V3Baxv3MzYvPahJWZGklr5IgL58wSiunT6Me5Zs4dEVOyJd\njoRQsN07gzOSKchK5q3NZbyz7VDMtPJBLX2RgJkZP/jUaZRW1vPdZz+gICs5pv5nl65V+m/kBuOM\nIVm8tM7ftRMj/fmglr5IUOLjfPzm2ilMKszizj/8k/dPsra6xIbWVkdVfXAtfWibpAUwIDWBKcMG\nhKO0sFDoiwQpNTGehTcWkZOWyNyHVrGnous9UyX6Vdc34xxB3ciFI5O0Lhw3uMvN1KORQl+kBwZn\nJPPAzVOpb2ph7gOrqKrXGP5Y1b7YWiBLMBztzKHZjM/P4JqpQ8NRVtgo9EV6aGxeBvdefzZbDtRw\nx2Oru9xNSaJbZRCLrR0tIzmBF78+mxkjB4ajrLDpNvTNbLGZ7TezD446lmNmL5vZR/5/Y6dDSySE\nZo4exA8/czpLPyrj//75A5zTGP5Yc2SxteBCP1YF0tJ/ELjsuGPfBl51zo0BXvU/FvGka4qG8pWL\nRvN48S7+99XNkS5HghTMYmv9Qbeh75x7Ezh03OErgYf8nz8EXBXiukRiyt2XjOUzZxXyy1c+5IlV\nuyJdjgShp907saqn4/TznHP7/J+XABqsLJ5mZvz46jM4UN3Av//5fXIzk7hwXOyM3fYyr4V+r2/k\nurZOzC47Ms1svpkVm1nxgQPadFr6r4Q4H/dcfzbj8zO447HVne6jKtGnsq6JpHgfyQndL6vcH/Q0\n9EvNrADA/+/+rk50zi1wzhU554pyc6N/rWmR3khPiueBm6YyIDWRuQ+uYsuBmkiXJN2orA1+YlYs\n62noPwfc6P/8RuDZ0JQjEvsGZybz8C3TALj2/hXsOHg4whXJyVTUNXpm5A4ENmTzj8ByYJyZ7Taz\nW4AfAZeY2UfAxf7HIuI3KjedR+dNp6G5lWvvX8nucm25GK2CXWwt1gUyeueLzrkC51yCc26Ic26R\nc+6gc26Oc26Mc+5i59zxo3tEPG98fiaP3jKdqvomrlu4kpLK+kiXJJ2orGsmKyW42bixTDNyRcJo\nUmEWD8+dRll1A9cuXKHgj0KVtY1q6YtI6EwZNoAHbp5GaWU9V9/zNpv36+ZuNFH3joiE3LQROTx+\n2zk0NLfw2XvfZvXO8kiXJEBTSyuHG1t0I1dEQm9SYRZP/8u5ZKUkcO39K3htY2mkS/I8r03MAoW+\nSJ86dWAaT91+LqMHp3Prw+/y8PLtWqQtghT6IhJ2uRlJ/Gn+OZw/NpfvPruObzzxHnWNLZEuy5M6\nFltT946IhFN6UjwLv1TEXReP5c9r9vCZe95m50GN5e9rVWrpi0hf8fmMr108hsU3TmVPeS1X/GYp\nr6xXP39f6lhLX6EvIn3lwvGDef4rsxgyIJV5Dxfznb98oO6ePlJR27ZVolr6ItKnhg1M5c93nMut\ns0bwyIodfPK3y1i/tyrSZfV7lXXNQPCboscyhb5IlEiKj+M/PjGRR26ZRlVdE1f97i0WvLmFllaN\n7gmXirpG0pPiSYjzThR65zsViRGzxuTy4tdnc8G4XH74wkY+d+/bWqI5TLw2GxcU+iJRKSctkftu\nOJtfff5Mthw4zOW/XqpWfxhUKfRFJFqYGVdNKeTlu2Yza0xbq//qe95mY4n6+kOlwmMbqIBCXyTq\nDc5M5v4vnc2vv3AmOw/VcsX/LuMnL26kvkkjfHpL3TsiEpXMjCvPLOSVu8/nqimF/H7JFj72qzdZ\n+pH2ne6NyromTy22Bgp9kZiSk5bIzz43mT/cOh2fGTcseoc7/7Ba6/T3UIVa+iISC84dNYi/f20W\nd108lpfXlzLn50tY8OYWmlpaI11azKhvaqGxudVTY/RBoS8Ss5IT4vjaxWN4+a7zmTFyID98YSOX\n/3opb20ui3RpMaF9sTV174hITBk2MJVFN01l4ZeKqG9u4bqFK7ntkWJ2HdICbu2aW1r5xhPvsWr7\nke28vbisMij0RfqNiyfm8fJd5/OvHxvHmx+WMecXb/DTlzZyuKE50qVF3NKPynh69W5+8uLGjmNH\nFlvzzqbooNAX6VeSE+K448LRvP7NC7h8Uj6/e30LF/xsCX9YuZNmD/f3P1G8C4BV28t5f3cl4M3F\n1kChL9Iv5Wcl86svTOGZL5/LqTmp/J8/v8/Hf72U1zaWem6nroM1DbyyoZTPFw0lLTGOB97aBqh7\nR0T6obOGDeDJ28/h3uvPprnVMffBYj6/YAUrtx6MdGl95i9r9tLU4ph73gg+VzSUv67dy/6q+iOh\nrxu5ItKfmBmXTcrnH3fN5gdXnsb2ssN8fsEKrl+4ktU7yyNdXlg553iyeBeTh2YzLj+DG88dTnOr\n49GVO6msa8IMMpLiI11mn1Loi3hEQpyPL50znDf/7UL+7ycmsGFfFZ/5/dvcuPgd3tpc1i+7fdbu\nrmRjSTXXFA0BYMSgNOaMH8xjK3awv6qBrJQEfD6LcJV9y1u/4kSE5IQ45s0ayRenDeOh5dtZvGw7\n1y1cyfj8DObNGsmnJp9CYnx0tQcP1jTw0f4aMpMTyEyJJzMlgeT4OBwO56DVORLifCesi/9E8S6S\n4n18cvIpHcdunjmCVzas5Pm1exmUkdTX30rE9Sr0zWw7UA20AM3OuaJQFCUi4ZeWFM+XLxjN3Jkj\neG7NXhYu28o3n3yPH/19I5+ecgpXnz2E8fmZkS6T3eW1XPW7tyiraTzpeelJ8fznVafx6Sltrfq6\nxhaeW7OXy08vIDP5SL/9uaMGMi4vg02l1Yzy2E1cCE1L/0LnnKYAisSo5IQ4rpk6lM8VDWHpR2U8\nsmIHD7y1nfuXbmNiQSafOav2qGkJAAAHtUlEQVSQyyblM2RAakjezznHzkO1DMtJxezkXSuHG5qZ\n91AxDU2t3Hv9WQBU1TVTVd9EfVMLZobPDDN4bcN+7nr8PVZtL+e7V0zkpXUlVDc08zl/1047M+Pm\nmcP59jPve27kDqh7R0T8zIzZY3OZPTaXQ4cb+et7e3l69W7+628b+K+/bWBCQSaXTMzjkgl5TCrM\n7DawO+Oc43vPrePh5TuYOnwAd10ylnNHDer03NZWx9cfX8OHpdU8cPM0zh+be9KvPe+8EfzsHx9y\n7xtbWLu7Ap8ZQ3NSmDFi4AnnXjWlkJ++tIm8zOSgv4dYZ725eWNm24BywAH3OecWnOz8oqIiV1xc\n3OP3E5G+t63sMK+sL+Xl9aUU7zhEq2u7IXr1WYV8+qwhFGanBPy1fvLixrZloU/LY82uCkqrGpgx\nMoe7Lh7LtBE5x/wi+fGLG7lnyRa+/8mJ3DRzRMDv8fL6Ur7xxBqq6pu5+5KxfHXOmE7P23mwlrSk\nOAamR3+/vpm9G6ru896GfqFzbo+ZDQZeBr7inHvzuHPmA/MBhg0bdvaOHTt6U6+IRNChw428sr6U\nZ/65mxVbD2EG54wcyKcmn8KcCXnknuTG6O+XbOYnL27i2unD+O+rJtHQ3Mof39nJ75ds4UB120ia\nSYWZTCrMIjHOx29e29xxbrB/Vew6VMsjK3bw5QtGkZ0a+8ssRE3oH/OFzL4P1DjnftbVOWrpi/Qf\nuw7V8szqPTy9ejc7D9ViBmcOzebiCXnMGJlDYXYquRlJxPmMR5Zv5zvPruPKM0/hF9ecSdxRwyTr\nm9puuP5zVznv76lkU0k1TS2Oc0cN5KG5004YkeNFURH6ZpYG+Jxz1f7PXwZ+4Jx7savXKPRF+h/n\nHBtLqnllfSmvbCjlPf/aNgDxPiMvM5k9FXVcPGEw91x/drch3tDcws6DtYwYlEa8Ah+IntAfCfzZ\n/zAe+INz7r9P9hqFvkj/V1pVz/q9VeypqGOv/yM7NZFvf3w8yQlxkS4vJoUy9Hs8esc5txWYHIoi\nRKT/yMtM9uSomFihv51ERDxEoS8i4iEKfRERD1Hoi4h4iEJfRMRDFPoiIh6i0BcR8RCFvoiIhyj0\nRUQ8RKEvIuIhCn0REQ9R6IuIeIhCX0TEQxT6IiIeotAXEfEQhb6IiIco9EVEPEShLyLiIQp9EREP\nUeiLiHiIQl9ExEMU+iIiHqLQFxHxEIW+iIiHKPRFRDxEoS8i4iEKfRERD1Hoi4h4SK9C38wuM7NN\nZrbZzL4dqqJERCQ8ehz6ZhYH/A74ODAR+KKZTQxVYSIiEnq9aelPAzY757Y65xqBPwFXhqYsEREJ\nh/hevLYQ2HXU493A9ONPMrP5wHz/wwYz+6AX7xmILKAyzK/t7ryTPd/Vc50dP/7Y8Y8HAWUnrbT3\nYvF69uRYX1zLruoI9et6ej31s9mz8/rieo7rpobAOed69AF8Flh41OMbgN9285rinr5fEHUtCPdr\nuzvvZM939Vxnx48/1sljXc8Arlsgx/riWvbmegbzup5eT/1s9uy8WLuevene2QMMPerxEP+xSPtr\nH7y2u/NO9nxXz3V2/PhjvfneeioWr2dvjoVbT98zmNf19HrqZ7Nn58XU9TT/b5HgX2gWD3wIzKEt\n7FcB1zrn1p3kNcXOuaIevaGcQNczdHQtQ0vXM7RCeT173KfvnGs2szuBl4A4YPHJAt9vQU/fTzql\n6xk6upahpesZWiG7nj1u6YuISOzRjFwREQ9R6IuIeIhCX0TEQ6Im9M1smJn9xcwWax2f3jGzWWZ2\nr5ktNLO3I11PrDMzn5n9t5n9xsxujHQ9sc7MLjCzpf6f0QsiXU+sM7M0Mys2sysCOT8koe8P6v3H\nz7YNckG204GnnHNzgSmhqCsWheJaOueWOuduB54HHgpnvdEuRD+bV9I2D6WJtpnnnhWi6+mAGiAZ\nD1/PEF1LgG8BTwT8vqEYvWNms2n7j/iwc26S/1gcbeP4L6HtP+wq4Iu0De/8n+O+xFygBXiKth+I\nR5xzD/S6sBgUimvpnNvvf90TwC3Oueo+Kj/qhOhncy5Q7py7z8yecs59tq/qjzYhup5lzrlWM8sD\nfuGcu66v6o8mIbqWk4GBtP0CLXPOPd/d+/Zm7Z0Ozrk3zWz4cYc7FmQDMLM/AVc65/4HOOHPEDP7\nJvA9/9d6CvBk6IfiWvrPGQZUejnwIWQ/m7uBRv/DlvBVG/1C9fPpVw4khaPOWBCin80LgDTaVjqu\nM7MXnHOtJ3vfkIR+FwJakO0oLwLfN7Nrge1hrCsWBXstAW7Bo784AxDs9XwG+I2ZzQLeDGdhMSqo\n62lmnwE+BmQDvw1vaTEnqGvpnPsPADO7Cf9fUN29QThDPyjOuQ9oW8RNQsA5971I19BfOOdqafsl\nKiHgnHuGtl+kEiLOuQcDPTeco3eidUG2WKRrGVq6nqGl6xk6Yb+W4Qz9VcAYMxthZonAF4Dnwvh+\n/ZmuZWjpeoaWrmfohP1ahmrI5h+B5cA4M9ttZrc455qB9gXZNgBPBLAgm+fpWoaWrmdo6XqGTqSu\npRZcExHxkKiZkSsiIuGn0BcR8RCFvoiIhyj0RUQ8RKEvIuIhCn0REQ9R6IuIeIhCX0TEQxT6IiIe\n8v8BNyNSFNRY/lIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4uh-97bpLZCA",
        "colab_type": "code",
        "outputId": "3bf4e89f-d415-4476-879c-4b0eaddc84b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "tf.random.set_seed(51)\n",
        "np.random.seed(51)\n",
        "#batch_size = 16\n",
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv1D(filters=32, kernel_size=3,\n",
        "                      strides=1, padding=\"causal\",\n",
        "                      activation=\"relu\",\n",
        "                      input_shape=[None, 1]),\n",
        "  tf.keras.layers.LSTM(32, return_sequences=True),\n",
        "  tf.keras.layers.LSTM(32, return_sequences=True),\n",
        "  tf.keras.layers.Dense(1),\n",
        "  tf.keras.layers.Lambda(lambda x: x * 200)\n",
        "])\n",
        "\n",
        "optimizer = tf.keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
        "model.compile(loss=tf.keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "history = model.fit(dataset,epochs=500)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "31/31 [==============================] - 4s 125ms/step - loss: 21.5713 - mae: 22.3311\n",
            "Epoch 2/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 8.4089 - mae: 8.8995\n",
            "Epoch 3/500\n",
            "31/31 [==============================] - 1s 34ms/step - loss: 6.7414 - mae: 7.2162\n",
            "Epoch 4/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 6.2969 - mae: 6.7814\n",
            "Epoch 5/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 5.7621 - mae: 6.2354\n",
            "Epoch 6/500\n",
            "31/31 [==============================] - 1s 34ms/step - loss: 5.6883 - mae: 6.1616\n",
            "Epoch 7/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 5.4758 - mae: 5.9522\n",
            "Epoch 8/500\n",
            "31/31 [==============================] - 1s 34ms/step - loss: 5.3304 - mae: 5.8025\n",
            "Epoch 9/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 5.3266 - mae: 5.8008\n",
            "Epoch 10/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 5.2538 - mae: 5.7294\n",
            "Epoch 11/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 5.1483 - mae: 5.6234\n",
            "Epoch 12/500\n",
            "31/31 [==============================] - 1s 34ms/step - loss: 5.1188 - mae: 5.5914\n",
            "Epoch 13/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 5.0646 - mae: 5.5368\n",
            "Epoch 14/500\n",
            "31/31 [==============================] - 1s 33ms/step - loss: 5.0190 - mae: 5.4924\n",
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            "Epoch 499/500\n",
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            "Epoch 500/500\n",
            "31/31 [==============================] - 1s 34ms/step - loss: 3.9464 - mae: 4.4087\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKkic-mLdkRZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
        "rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4N1toSetdnQq",
        "colab_type": "code",
        "outputId": "2fd5cf78-6707-4bb2-9c09-8ef72eb4f6aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, rnn_forecast)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UHGfHP76aP3nJsrLlMaP8ccNZk0/ftb0sxwxKOWfzG2Ok8ibrD/YF9xUsyUETBEGYbizH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\nUVSlPCQinF38fV8W1jzFMJx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oq4sS0dRSWHMM5+zRPW4CfngeaiW9I3l+/9b1/HTjsXLn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\njAAAAAtJREFUREREpI78fyisI+fESFQ0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULKO3JINdqkp",
        "colab_type": "code",
        "outputId": "92501507-62f4-4821-b93c-1dbdbbb2a64a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.0066857"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ok8LjNbbkig4",
        "colab_type": "code",
        "outputId": "e6749235-530b-41e8-b709-a430eaea15e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        }
      },
      "source": [
        "import matplotlib.image  as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "mae=history.history['mae']\n",
        "loss=history.history['loss']\n",
        "\n",
        "epochs=range(len(loss)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot MAE and Loss\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, mae, 'r')\n",
        "plt.plot(epochs, loss, 'b')\n",
        "plt.title('MAE and Loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend([\"MAE\", \"Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "epochs_zoom = epochs[200:]\n",
        "mae_zoom = mae[200:]\n",
        "loss_zoom = loss[200:]\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot Zoomed MAE and Loss\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs_zoom, mae_zoom, 'r')\n",
        "plt.plot(epochs_zoom, loss_zoom, 'b')\n",
        "plt.title('MAE and Loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend([\"MAE\", \"Loss\"])\n",
        "\n",
        "plt.figure()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GRmw7FSqvqen2czez3m27CIytKiFdBqyJiP/OK7sBeDwiZqfLfwIOi4iij16X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\nMzPLxIFhZmaZODDMzCyT/w+PzyOpo4IAYwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZaClcCZQAVXUq/83dL89APaQ+6NoVTj89lJj9+2HNGli8uHxYPPlkYunwli3D1U8Vw0Jd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\npYPU/WiAZcIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}